Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D scene decomposition, not industrial defect inspection.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a 3D reconstruction system, not an evaluation benchmark.
Unravel: Recovering an Interactive 3D Scene from a Single Image This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D computer graphics reconstruction, not safety.
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training This paper introduces WildTrace, a benchmark evaluating training data detection methods across realistic, multi-stage language model post-training pipelines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient inference.
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training This paper introduces WildTrace, a benchmark evaluating training data detection methods across realistic, multi-stage language model post-training pipelines. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies training data detection rather than agent execution.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies theoretical loss geometry rather than agent systems.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on learning theory rather than model compression techniques.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of memorization, not an evaluation benchmark.
Fisher information and the geometry of memorization in neural networks This paper analyzes Fisher information eigenvectors to show how neural networks geometrically decouple generalizable patterns from memorized noisy data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of capacity allocation, not deployment safety.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a physics modeling task, not an LLM benchmark.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient serving.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with fluid dynamics simulation rather than AI safety.
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction This paper proposes a task and framework to forecast dense 3D liquid velocity fields based on how solid objects move through them. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies physical fluid dynamics forecasting, not LLM agents.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general video understanding rather than industrial inspection.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on video representation learning rather than multi-step agent planning.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a reinforcement learning framework, not a general evaluation benchmark.
EventLens: Event-Structure Reinforcement Learning for Video Understanding This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses temporal representation learning rather than safety or guardrails.
ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contracts This paper proposes ProCARE, a framework that uses structured contracts to reliably automate healthcare observational studies with language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on execution framework rather than general LLM evaluation.
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on classification efficiency, not safety or guardrails.
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on zero-shot classification retrieval, not multi-step agents.
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification This paper proposes a framework that precomputes label definitions and boundaries with large models to enable cheaper zero-shot text classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a classification method, not an evaluation benchmark.
Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces This paper introduces Choice2Seq, a benchmark measuring how often language models retain correct multiple-choice answers when generating them step by step. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates interface formatting consistency rather than deployment safety risks.
Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces This paper introduces Choice2Seq, a benchmark measuring how often language models retain correct multiple-choice answers when generating them step by step. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates interface outputs, not tool-using or autonomous agents.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Spatial transcriptomics method, not an AI agent system.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Biology application model, not an LLM evaluation method.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on biological domain identification, not model compression.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to biological transcriptomics, not industrial visual inspection.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with spatial transcriptomics data, not multimodal language models.
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Biological modeling unrelated to deployment safety and robustness.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses embedded system scheduling, not language model agents.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Embedded hardware exploration framework, not an LLM benchmark.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to LLMs writing or fixing code.
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version) Presents a design space exploration tool for mapping, scheduling, and generating safety arguments for dataflow applications on embedded hardware platforms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on embedded hardware dataflow architectures, not AI deployment safety.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant General time-series work unrelated to LLM deployment safety.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on time-series ensembling, not model compression.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses time-series anomaly detection, not LLM agents.
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection Weights an ensemble of time-series anomaly detectors using confirmation of a single reported anomaly from a human operator. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a detector ensembling method, not an LLM benchmark.
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent framework, not a benchmark.
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on image editing performance, not AI safety.
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative image composition, not industrial defect detection.
Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks Presents a framework for developing language model facilitators that balance group dynamics, psychological safety, and task goals across multi-party conversations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-party facilitation rather than safety or guardrails.
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on biological optimization rather than AI deployment safety.
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses biological sequence generation, not model compression.
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms Uses agentic program search to iteratively write and refine design algorithms for nucleic acid sequences that transfer across independent models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a design method, not an LLM benchmark.
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling Proposes reflected proximal dynamics to combine exact target preservation with momentum-based acceleration in log-concave sampling. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with log-concave sampling theory, not model efficiency.
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling Proposes reflected proximal dynamics to combine exact target preservation with momentum-based acceleration in log-concave sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an optimization/sampling algorithm, not an evaluation benchmark.
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling Proposes reflected proximal dynamics to combine exact target preservation with momentum-based acceleration in log-concave sampling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical sampling work unrelated to deployment safety.
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling Proposes reflected proximal dynamics to combine exact target preservation with momentum-based acceleration in log-concave sampling. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical sampling theory, not AI agents.
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation Measures teacher entity familiarity to filter out uninformative search trajectories when distilling deep-search capabilities into smaller agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text entity graphs, not multimodal models.
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation Measures teacher entity familiarity to filter out uninformative search trajectories when distilling deep-search capabilities into smaller agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on distillation data quality, not deployment safety.
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation Measures teacher entity familiarity to filter out uninformative search trajectories when distilling deep-search capabilities into smaller agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a distillation filtering method rather than an evaluation benchmark.
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation Measures teacher entity familiarity to filter out uninformative search trajectories when distilling deep-search capabilities into smaller agents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on coding assistants or software engineering.
ScreenASR-Bench: A Tiered Benchmark for Speech Recognition with Visual Context Introduces a tiered benchmark to assess how explicit and implicit visual evidence helps speech recognition models resolve ambiguous words on screens. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Benchmarks speech recognition, not autonomous agentic systems.
ScreenASR-Bench: A Tiered Benchmark for Speech Recognition with Visual Context Introduces a tiered benchmark to assess how explicit and implicit visual evidence helps speech recognition models resolve ambiguous words on screens. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on ASR evaluation, not safety or guardrails.
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses general super-resolution quantization, not industrial defect inspection.
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on quantization efficiency, not deployment safety or reliability.
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses low-level quantization, not language model agents.
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on vision super-resolution quantization, not multimodal models.
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a quantization method, not a benchmarking protocol.
A Survey of Flow Matching in Reinforcement Learning Surveys the use of flow-matching generative modeling approaches within reinforcement learning across policy parameterization and foundation model alignment. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Surveys generative modeling in RL, not multi-step LLM agents.

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